US10032079B2 - Evaluation of models generated from objects in video - Google Patents
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- US10032079B2 US10032079B2 US15/048,237 US201615048237A US10032079B2 US 10032079 B2 US10032079 B2 US 10032079B2 US 201615048237 A US201615048237 A US 201615048237A US 10032079 B2 US10032079 B2 US 10032079B2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/776—Validation; Performance evaluation
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- G06K9/00711—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
- G06T7/251—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments involving models
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- G06V10/40—Extraction of image or video features
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- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/103—Static body considered as a whole, e.g. static pedestrian or occupant recognition
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
- G06V40/23—Recognition of whole body movements, e.g. for sport training
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
- G06V40/23—Recognition of whole body movements, e.g. for sport training
- G06V40/25—Recognition of walking or running movements, e.g. gait recognition
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- G—PHYSICS
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
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- G06T2207/30241—Trajectory
Definitions
- aspects of the invention are related, in general, to the field of image processing and analysis.
- Image analysis involves performing processes on images or video in order to identify and extract meaningful information from the images or video. In many cases, these processes are performed on digital images using digital image processing techniques. Computers are frequently used for performing this analysis because large amounts of data and complex computations may be involved. Many image processing techniques are designed to emulate recognition or identification processes which occur through human visual perception and cognitive processing.
- a method of operating an image processing system comprises generating models from objects identified in video.
- the method further comprises evaluating each model based on knowledge of the objects determined from video analysis, and identifying at least one preferred model based on the evaluating.
- one or more computer readable media have stored thereon program instructions which, when executed by a processing system, direct the processing system to generate models from objects identified in video.
- the program instructions further direct the processing system to perform evaluations on each model based on knowledge of the objects determined from video analysis, and identify at least one preferred model based on the evaluations.
- an image processing system comprises a processing system.
- the processing system is configured to generate models from objects identified in video.
- the processing system is further configured to perform evaluations on each model based on knowledge of the objects determined from video analysis, and identify at least one preferred model based on the evaluations.
- tracking the movement of each object in the video comprises using each model to track the object from which it was generated.
- evaluating each model based on knowledge of the objects determined from video analysis comprises evaluating an ability of each model to identify the objects in the video that are similar to the object from which it was generated.
- evaluating each model based on knowledge of the objects determined from video analysis comprises determining an amount of false identifications made by each model of different objects in different video that does not include the object from which it was generated.
- evaluating each model based on knowledge of the objects determined from video analysis comprises tracking a movement of each object in the video by using each model to track the object from which it was generated, evaluating an ability of each model to identify the objects in the video that are similar to the object from which it was generated, and determining an amount of false identifications made by each model of different objects in different video that does not include the object from which it was generated.
- identifying at least one preferred model based on the evaluations comprises identifying a model having a greatest ability to identify the objects in the video that are similar to the object from which it was generated and having a least amount of false identifications of the different objects in the different video.
- the objects are identified in the video by manual identification.
- the objects are identified in the video by human head detection.
- the objects identified in the video comprise human body parts.
- FIG. 1 is a block diagram that illustrates an imaging system
- FIG. 2 is a flow diagram of a process according to an embodiment of the invention for operating an image processing system
- FIG. 3 is a block diagram that illustrates video and models generated from objects identified in the video
- FIG. 4 is a block diagram that illustrates video and an evaluation of a model based on knowledge of an object in the video
- FIG. 5 is a block diagram that illustrates video and an evaluation of models based on knowledge of objects in the video
- FIG. 6 is a block diagram that illustrates video and an evaluation of models based on knowledge of objects in the video
- FIG. 7 is a block diagram that illustrates an image processing system.
- a descriptors-based detection technique is employed to detect and identify objects using one or more of an object's parts. Models of the object are generated and then portions of images in video are compared to these predetermined models. Preferred models are selected intelligently based on their ability to maximize the detection rate of similar objects while keeping false detections to a minimum.
- FIGS. 1-2 are provided to illustrate one implementation of an imaging system 100 and its operation.
- FIG. 1 depicts elements of imaging system 100
- FIG. 2 illustrates process 200 that describes the operation of imaging system 100 .
- Imaging system 100 comprises video source 101 and image processing system 120 .
- Video source 101 may comprise any device having the capability to capture video or images.
- Video source 101 comprises circuitry and an interface for transmitting video or images.
- Video source 101 may be a device which performs the initial optical capture of video, may be an intermediate video transfer device, or may be another type of video transmission device.
- video source 101 may be a video camera, still camera, internet protocol (IP) camera, video switch, video buffer, video server, or other video transmission device, including combinations thereof.
- IP internet protocol
- imaging system 100 may contain additional video sources, additional image processing systems, or other devices.
- process 200 describes the operation of imaging system 100 in an implementation, and in particular, the operation of image processing system 120 .
- the steps of process 200 are indicated below parenthetically.
- each model is evaluated based on knowledge of the objects determined from video analysis ( 203 ).
- image processing system 120 could analyze the video in order to track movement of each object in the video.
- image processing system 120 could track the movement of each object in the video by using each model to track the object from which it was generated.
- this model evaluation technique tests the model's ability to track its associated object from which it was generated as the object moves and changes position in the video.
- a movement profile for each human could be generated based on each head model tracking the movement of its respective human through a video scene.
- Such tracking could provide statistics about the dynamics of the scene, such as average and maximum step size of each person, rates of speed, where most foot traffic occurs, and the like.
- Such motion dynamics could be stored in association with their respective models for later use in identifying different objects, such as the heads of different humans, which might appear in different video.
- image processing system 120 could evaluate each model based on knowledge of the objects determined from video analysis by evaluating an ability of each model to identify the objects in the video that are similar to the object from which it was generated. In this evaluation, each model is tested to determine its ability to detect and identify objects that are similar to the object from which it was modeled. For example, continuing the above example of human head modeling, each head model could be evaluated against video of other humans to see which of the other humans were correctly identified using the head models from different humans. In some examples, image processing system 120 could optionally determine which head models incorrectly detected body parts other than heads and/or other non-human objects as human heads.
- models of various objects appearing in video can be evaluated to determine preferred models that best detect similar objects in other video.
- the preferred models can be selected intelligently in order to maximize the detection rate while keeping false detections and the number of models to a minimum. In this manner, inferior models that are inaccurate and overly general are filtered out and eliminated so that a smaller collection of preferred, optimal models are identified and selected for use.
- FIG. 4 is a block diagram that illustrates video 400 and an evaluation of a model 311 based on knowledge of an object 301 in the video 400 .
- video 400 depicts a scene in which triangle object 301 is traveling in motion.
- Model 311 which was generated from object 301 previously based on video 300 of FIG. 3 , is used to track the movement of object 301 throughout the video scene 400 .
- triangle object 301 is being detected and tracked using its own model 311 .
- model 311 successfully tracks the movement of object 301 from which it was generated.
- FIG. 5 is a block diagram that illustrates video 500 and an evaluation of models 311 and 312 based on knowledge of objects 301 and 302 in the video 500 .
- This evaluation tests the ability of each model 311 and 312 to detect and identify objects 302 and 301 that are similar to the objects 301 and 302 that were used to generate their respective models 311 and 312 .
- model 311 since model 311 was generated from triangle object 301 , model 311 is evaluated to determine its ability to detect similar triangle object 302 in video 500 .
- triangle model 312 was modeled after triangle object 302 , so the ability of model 312 to detect similar triangle object 301 is tested.
- each model 311 and 312 successfully identifies a similar object 302 and 301 , respectively.
- model 311 correctly identifies triangle object 302 that is similar to triangle object 301 from which model 311 was generated.
- model 312 accurately identifies triangle object 301 that is similar to triangle object 302 from which model 312 was generated.
- FIG. 6 is a block diagram that illustrates video 600 and an evaluation of models 311 and 312 based on knowledge of objects 601 and 602 in the video 600 .
- models 311 and 312 were modeled after triangle objects 301 and 302 as discussed above with respect to FIG. 3 , the image in the video 600 does not contain any triangle objects. Instead, video 600 contains a circular object 601 and a square object 602 . Models 311 and 312 are thus evaluated against the scene in video 600 to determine if either model 311 or 312 falsely identifies one of the objects 601 or 602 as a triangle object.
- FIG. 7 illustrates image processing system 700 .
- Image processing system 700 provides an example of image processing system 120 , but image processing system 120 could have alternative configurations.
- Image processing system 700 and the associated description below are intended to provide a brief, general description of a suitable computing environment in which process 200 of FIG. 2 may be implemented. Many other configurations of computing devices and software computing systems may be employed to implement process 200 .
- Image processing system 700 may be any type of computing system capable of evaluating models generated from objects identified in video, such as a client computer, server computer, internet apparatus, or any combination or variation thereof. Image processing system 700 may be implemented as a single computing system, but may also be implemented in a distributed manner across multiple computing systems. Image processing system 700 is provided as an example of a general purpose computing system that, when implementing process 200 , becomes a specialized system capable of evaluating models generated from objects identified in video and identifying preferred models based on the evaluations.
- Image processing system 700 includes communication interface 710 and processing system 720 .
- Processing system 720 and communication interface 710 are in communication through a communication link.
- Processing system 720 includes processor 721 and memory system 722 .
- Memory system 722 stores software 723 , which, when executed by processing system 720 , directs image processing system 700 to operate as described herein for process 200 .
- Communication interface 710 includes network interface 712 , input ports 716 , and output ports 718 .
- Communication interface 710 includes components that communicate over communication links, such as network cards, ports, RF transceivers, processing circuitry and software, or some other communication device.
- Communication interface 710 may be configured to communicate over metallic, wireless, or optical links.
- Communication interface 710 may be configured to use TDM, IP, Ethernet, optical networking, wireless protocols, communication signaling, or some other communication format, including combinations thereof.
- Image processing system 700 may include multiple network interfaces.
- Network interface 712 is configured to connect to external devices over network 770 .
- Network interface 712 may be configured to communicate in a variety of protocols.
- Input ports 716 are configured to connect to input devices 780 such as a video source, a storage system, a keyboard, a mouse, a user interface, or other input device.
- Output ports 718 are configured to connect to output devices 790 such as a storage system, other communication links, a display, or other output devices.
- Processing system 720 includes processor 721 and memory system 722 .
- Processor 721 includes microprocessor or other circuitry that retrieves and executes operating software from memory system 722 .
- Processor 721 may comprise a single device or could be distributed across multiple devices—including devices in different geographic areas.
- Processor 721 may be embedded in various types of equipment.
- Memory system 722 may comprise any storage media readable by processing system 720 and capable of storing software 723 , including operating system 724 , applications 725 , model creation module 728 , and model testing module 729 .
- Memory system 722 may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data.
- Memory system 722 may comprise a single device or could be distributed across multiple devices—including devices in different geographic areas.
- Memory system 722 may be embedded in various types of equipment.
- Memory system 722 may comprise additional elements, such as a controller, capable of communicating with processing system 720 .
- Model creation module 728 and model testing module 729 comprises computer program instructions, firmware, or some other form of machine-readable processing instructions having process 200 embodied therein.
- Model creation module 728 and model testing module 729 may be implemented as a single application but also as multiple applications.
- Model creation module 728 and model testing module 729 may be stand-alone applications but may also be implemented within other applications distributed on multiple devices, including but not limited to program application software and operating system software.
- software 723 may, when loaded into processing system 720 and executed, transform processing system 720 , and image processing system 700 overall, from a general-purpose computing system into a special-purpose computing system customized to evaluate models generated from objects identified in video and identify preferred models based on the evaluations as described by process 200 and its associated discussion.
- Software 723 , and model creation module 728 and model testing module 729 in particular, may also transform the physical structure of memory system 722 .
- the specific transformation of the physical structure may depend on various factors in different implementations of this description. Examples of such factors may include, but are not limited to, the technology used to implement the storage media of memory system 722 , whether the computer-storage media are characterized as primary or secondary storage, and the like.
- Software 723 comprises operating system 724 , applications 725 , model creation module 728 , and model testing module 729 .
- Software 723 may also comprise additional computer programs, firmware, or some other form of non-transitory, machine-readable processing instructions.
- operating software 723 When executed by processing system 720 , operating software 723 directs processing system 720 to operate image processing system 700 as described herein for image processing system 120 and process 200 .
- operating software 723 directs processing system 720 to generate models from objects identified in video.
- Operating software 723 also directs processing system 720 to perform evaluations on each model based on knowledge of the objects determined from video analysis. Further, operating software 723 directs processing system 720 to identify at least one preferred model based on the evaluations.
- operating software 723 comprises a model creation software module 728 that generates models from objects identified in video. Additionally, operating software 723 comprises a model testing software module 729 that performs evaluations on each model based on knowledge of the objects determined from video analysis and identifies at least one preferred model based on the evaluations.
Abstract
Description
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US10419788B2 (en) * | 2015-09-30 | 2019-09-17 | Nathan Dhilan Arimilli | Creation of virtual cameras for viewing real-time events |
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US10032080B2 (en) | 2018-07-24 |
US20170109583A1 (en) | 2017-04-20 |
US9268996B1 (en) | 2016-02-23 |
US10438066B2 (en) | 2019-10-08 |
US20180307910A1 (en) | 2018-10-25 |
US20160275373A1 (en) | 2016-09-22 |
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